The Open Microscopy Environment is a project started by Dr. Ilya Goldberg at MIT five years ago in the group of Dr. Peter Sorger. The purpose is to develop an information framework for computational cell biology - a sub-specialty of bioinformatics called 'Image Informatics'. This open-source framework consists of a database, several analytic modules, and an application program interface (API) that ties the modules to the database. The database provides a semantic framework and a data model for biological information obtained by analyzing images. It also keeps track of the images themselves, and of all the analyses performed on them. The database is also the communication link between analysis modules permitting the multiplexing of analysis algorithms. Finally, the entire system provides web-based and Java user interfaces allowing for remote interaction. This software functions as an image repository and management system for very large collections of scientific image data. This software is currently deployed in several research labs with considerable imaging needs, and includes repositories several terabytes in size. Over the past year we have made two public releases of this software targeted at end-users (version 2.2 and 2.4). These two releases focused on manual classification and annotation of image data. Ongoing developments include the use of the OME Analysis Engine to drive image analysis algorithms written in MATLAB. An experimental implementation of the MATLAB interface was made available on our software distribution site, and has attracted several users from academia and industry. It is anticipated that a stable release of this interface will be made available this year. The universal scientific image format developed for OME (OME XML) has been selected by the Association of Pathology Informatics as a new file format for medical imaging in pathology. It is anticipated that versions of this hybrid format will be made available over the coming year. This hybrid format will include sections from OME describing microscopes, digital acquisition systems, light paths and image data, while sections newly developed by the Association for Pathology Informatics will deal with medical meta-data. This year also saw the publication of a description of the OME XML format and the OME data model in the journal Genome Biology The second major effort in this project involves building information visualization tools for the OME platform. Dr. Harry Hochheiser has developed a visual work flow editor for OME. The chain builder allows the user to place analysis modules on a canvas and connect their inputs and outputs into work-flows (analysis chains) of arbitrary complexity for execution by OME. Dr. Hochheiser has also developed a tool to browse the "pseudo hierarchies" of data in OME, allowing the user to interactively and visually investigate the relationships between overlapping collections of images organized by projects, datasets, categories, etc. Additionally, Dr. Hochheiser developed a 3D viewer for image analysis results from the automated 3D particle tracker in OME. Work over the next year will include merging these visualization tools into an integrated data browser for OME. As part of the continuing effort in making this complex software framework accessible to end-users, Arpun Nagaraja and Josiah Johnston have developed two tools to help users organize and manage their image data. The first is a mechanism for end-users to control the type of data presented in the web user interface during browsing, searching and data entry. The second is a mechanism for reading and writing OME data from Microsoft Excel. In order to test these features on a specific project in biology, we are working together with the Developmental Genomics and Aging Section of LG. One of the projects in DGAS is to map the spatial expression patterns of developmental genes in mouse embryos and stem cells. We are working together with DGAS to develop OME as a platform for recording the results of these experiments as well as publishing them on-line. One of the outcomes of this project will be the integration of a public OME repository of in-situ image data together with the NIA Mouse Gene index, which contains information about the genes and probes used in these studies.